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Hepatitis C Virus Infection in Young, Low-Income
Women: The Role of Sexually Transmitted Infection as a Potential
Cofactor for Hepatitis C Virus Infection
Kimberly A. Page-Shafer, PhD, MPH, Barbara Cahoon-Young, PhD, Jeffrey
D. Klausner, MD, MPH, Scott Morrow, PhD, Fred Molitor, PhD, Juan Ruiz,
MD, DrPH and Willi McFarland, MD PhD for the Young Women's Survey Team
Kimberly A. Page-Shafer is with the Center for AIDS Prevention
Studies, Department of Medicine, University of California San Francisco,
Calif. Barbara Cahoon-Young is with East Bay Liver Clinic, Oakland,
Calif. Jeffrey D. Klausner is with the Division of STD Control, San
Francisco Department of Public Health, San Francisco, Calif. Scott
Morrow is with San Mateo County Department of Public Health, Redwood
City, Calif. Fred Molitor and Juan Ruiz are with the Office of AIDS,
HIV/AIDS Epidemiology Branch, California Department of Health Services,
Sacramento, Calif. Willi McFarland is with the HIV Epidemiology Unit,
San Francisco Department of Public Health, San Francisco, Calif.
Correspondence: Requests for reprints should be sent to Kimberly A.
Page-Shafer, PhD, MPH, Center for AIDS Prevention Studies, UCSF, 74 New
Montgomery St, Suite 500, San Francisco, CA 94105 (e-mail:
Objectives. We evaluated risk for hepatitis C virus (Hepatitis C Virus)
infectionin women residing in low-income neighborhoods of
northern California.
Methods. A population-based sample of 1707 women, aged 18 to29, were surveyed and screened for sexually transmitted infectionsand Hepatitis C Virus.
Results. Women infected with Hepatitis C Virus (2.5%) were more likely tohave a history of injection and noninjection drug use, to exchangesex for money or drugs, and to have sexually transmitted
infections.Hepatitis C Virus was independently associated with history of
injection druguse, herpes simplex virus type 2 (HSV-2)
infection, and heroinand cocaine use.
Conclusions. Injection drug use is the highest risk exposurefor Hepatitis C Virus, but HSV-2 and noninjection drug use contribute
significantlyto increased risk. Hepatitis C Virus prevention programs in
impoverished areasshould integrate drug treatment and
sexually transmitted infectioncontrol.
INTRODUCTION
Hepatitis C virus (Hepatitis C Virus) is the most important cause of acute
and chronic liver disease in the United States. An estimated
4 million people, 1.8% of the US general population, are Hepatitis C Virus
infected.1
Persistent infection develops in more than 85% ofthe persons
exposed. Chronic hepatitis develops in 50% to 70%of the
infected persons, and 10% to 20% of these may go on to
develop cirrhosis.2
Liver failure and hepatocellular carcinomasnecessitating
liver transplantation are some of the most severe
consequences of Hepatitis C Virus infection. An estimated 8000 to 10 000 deathsper year are attributed to Hepatitis C Virus-associated liver disease, a figureexpected to triple in the next 10 to 20 years.3
Given the currentlow response to treatment (< 50%),
primary preventionremains the most important public health
control strategy toreduce Hepatitis C Virus-related morbidity.
Hepatitis C Virus infection is most easily acquired parenterally. As a result,prevalence is highest among injection drug users (IDUs) andhemophiliacs.
Nonparenteral transmission of Hepatitis C Virus appearsto be inefficient.
Past research has documented thecofactor role of sexually
transmitted infections in amplifyingthe acquisition and
transmission of HIV and hepatitis B virus(HBV),
but this interrelationship has not been wellexamined for Hepatitis C Virus.
High rates of sexually transmitted infectionsand Hepatitis C Virus
coinfection among IDUs suggest that ulcerative or nonulcerativeurogenital infections may be cofactors for Hepatitis C Virus transmission.However, investigation of sexually transmitted infections aspotential cofactors for sexual transmission of Hepatitis C Virus is hamperedby the confounding effects of concomitant high-risk sexual
behaviorand injection practices.
Lack of data on the determinantsof sexual transmission of
Hepatitis C Virus has limited the development ofguidelines for sexual
partners who may be at risk for transmittingor acquiring
Hepatitis C Virus.
The current study examined Hepatitis C Virus in the Young Women's Survey,a population-based sample of young women recruited in lowincome,multiethnic neighborhoods of northern California.20
Analysisfocused on sexual behavior and sexually transmitted
infectionsas risk factors for Hepatitis C Virus and their associated
population attributablefractions.
METHODS
Study Design
The Young Women's Survey was a single-stage, cluster-sample,
population-based, door-to-door, cross-sectional survey designedto measure the prevalence of HIV, sexually transmitted diseases,and related risk behavior in young, low-income women in northernCalifornia. The Young Women's Survey study methods, study
population,and primary outcomes have been described in
detail elsewhere.Hepatitis C Virus testing was conducted on stored sera from participants in4 counties: Alameda, San Francisco, San Joaquin, and San Mateo.
Study Subjects
The target population was young women residing in low-income
neighborhoods. Eligibility criteria were being female, aged
18 to 29 years, fluent in English or Spanish, and a resident
in the target area. The target area was defined as 1990 US censusblock groups below the 10th percentile for median householdincome. In the 4 counties included in the study of Hepatitis C Virus, a totalof 19 270 inhabited dwellings were enumerated in 276 randomlyselected street blocks within the target area. Contact was madewith a resident in each of 15 943 dwellings (82.7%). Of the2828 eligible women identified, 2096 (74.1%) were enrolled fromApril 1996 to January 1998. Sera were available for 1707 (81.4%)of the women who were interviewed.
Measures
A structured interviewer-administered survey was conducted to
gather data on sociodemographic characteristics, sexual behavior,substance use, medical history, and other health-related factors.Response rates for most variables were greater than 99%. Bloodand urine samples were obtained to test for HIV, syphilis, herpessimplex virus types 1 and 2 (HSV-1, HSV-2), HBV, gonorrhea,and chlamydia.
Laboratory Methods
Antibody to Hepatitis C Virus (anti-Hepatitis C Virus) was detected with a third-generationenzyme immunoassay (EIA-3.0; Ortho Diagnostics Systems, Raritan,NJ). Specimens reactive by initial EIA-3.0 were confirmed witha strip recombinant immunoblot assay (RIBA 2.0; Chiron,
Emeryville,Calif). Discrepant results (EIA+, RIBA–) were
considerednegative. HIV testing was conducted with enzyme
immunoassay(EIA; Abbott Laboratories, Abbott Park, Ill) and
confirmed byimmunofluorescent antibody (IFA; Neufeld,
Vienna, Austria).Antibody to hepatitis B core antigen (anti-HBc)
was detectedby EIA (Abbott Laboratories, Abbott Park, Ill),
and hepatitisB surface antigen (HBsAg) was detected by
microparticle EIA(Abbott Laboratories, Abbott Park, Ill).
HSV-1 and HSV-2 specificantibodies were differentiated based
on recombinant antigenbands for gG1, gB1, gG2, and gD2 with
a strip recombinant immunoblotassay (RIBA HSV Type 1/Type 2
SIA; Chiron, Emeryville, Calif).Blood samples were tested
for syphilis by rapid plasma reaginor VDRL tests; reactive
specimens were confirmed by microhemagglutinationtest for
Treponema pallidum. Ligase chain reaction (LCx; Abbott
Laboratories, Abbott Park, Ill) was used to detect gonococcal
and chlamydial DNA in urine specimens.
Statistical Methods
To account for the single-stage, cluster-sample survey design,we used Stata, Version 6.0, Survey (SVY) procedures to constructpoint prevalences, 95% confidence intervals (CIs), and oddsratios (ORs).
Ninety-five-percent confidence intervals wereadjusted to
account for homogeneity within the primary samplingunits
(i.e., city blocks). Because crude prevalence estimatesin
the sample differed from the survey-adjusted estimates, we
present only weighted percentages.
Multiple logistic regression analysis, adjusting for the surveydesign, was used to identify independent correlates of Hepatitis C Virus
infectionbased on factors significant in bivariate analyses,
a priorihypotheses (such as coinfection with HIV or HBV),
and othervariables of interest or potential confounders
(such as age,race/ethnicity, and county). Models were
examined with botha backward and a forward stepwise process.
Variables were retainedin the models if they reached a
significance level of .05 orless. The final multiple
logistic model and aflogit proceduresemploying Stata statistical software were used to obtain estimatesof adjusted population attributable fraction and corresponding95% confidence intervals with an approach based on unconditionallogistic regression.
The 95% confidence intervals associatedwith the population
attributable fraction estimates were adjustedfor probability
weights but not for the cluster weights.
RESULTS
Prevalence of Anti-Hepatitis C Virus, by Social and Demographic
Characteristics
The population-based estimate of Hepatitis C Virus prevalence among women
aged 18 to 29 years in low-income neighborhoods of the 4-countytarget area was 2.5% (95% CI = 1.4, 3.6) (Table 1).
The estimateis based on the 40 Hepatitis C Virus RIBA-confirmed specimens
of a total of63 found to be positive with EIA-3.0. More than
a third (39.2%)of the subjects were African American, 31.9%
were Latina, 15.4%were White, 6.7% were Asian or Pacific
Islander, and 6.7% indicatedother or mixed
race/ethnicity. Most women (70.5%) were bornin the United
States; 16.9% were born in Mexico, and 12.5% wereborn in
other countries. The median age was 23.9 years (interquartile
range = 21.0–26.7).
TABLE 1—Prevalence of Hepatitis C Virus (Hepatitis C Virus) Infection, by
Demographic Characteristics, in Women Aged 18 to 29 Years From
Low-Income Neighborhoods of 4 Northern California Counties, April
1996–January 1998
Population Prevalence of Variable, %a
Population Prevalence of Hepatitis C Virus Antibody, % (95% CI)a,b OR (95% CI)a
Total 2.5 (1.4, 3.6)b
County of residence
Alameda
30.0 3.8 (1.7, 6.0)
8.9 (2.6, 29.7)*
San Francisco
27.9 4.3 (1.4, 7.1)
10.0 (2.8, 35.4)*
San Joaquin
13.2 1.4 (0.1, 2.7)
Referent
San Mateo
28.9 0 (NAc)
NAc
Monthly household income, $
0–499
25.9 5.1 (2.4, 7.8
5.6 (2.1, 14.7)*
500–999
33.7 2.2 (0.8, 3.7)
2.4 (0.9, 6.2)
1000–2999
33.0 1.1 (0.2, 2.1)
Referent
3000
7.3 0 (NAc)
NAc
Race/ethnicity
White
15.4 3.8 (1.1, 6.4)
5.3 (1.3, 21.5)***
African American
39.2 4.0 (2.0, 5.9)
5.6 (1.8, 17.5)**
Asian or Pacific Islander
6.7 0.9 (0, 2.5)
1.2 (0.1, 11.6)
Other
6.7 1.7 (0, 4.0)
2.3 (0.4, 15.0)
Latina
31.9 0.7 (0, 1.6)
Referent
Education
< High school
40.6 3.1 (1.4, 4.8)
5.7 (0.9, 36.8)
High school graduate
27.0 2.6 (1.1, 4.1)
4.7 (0.7, 32.1)
Vocational or some college
22.2 2.4 (0.7, 4.0)
4.3 (0.5, 35.0)
College degree
10.2 0.6 (0, 1.7)
Referent
Marital status
Currently married
19.4 1.9 (0, 4.1)
0.7 (0.2, 2.2)
Previously married
8.4 4.9 (1.1, 8.6)
1.8 (0.8, 4.3)
Unmarried partnership
10.1 0.6 (0, 1.7)
0.2 (0.3, 1.4)
Single
62.1 2.7 (1.4, 4.0)
Referent
Note. CI = confidence interval; OR = odds ratio.
a
All prevalence estimates, 95% CIs, and ORs are adjusted
for the survey design.
b
Anti-Hepatitis C Virus confirmed in n = 40.
c
Not able to calculate survey-adjusted CIs or ORs when no
infections were detected.
*P.001;
**P.01;
***P.05.
The prevalence of Hepatitis C Virus varied significantly by county of residence,income level, and race/ethnicity. Hepatitis C Virus prevalence was highestin the 2 most urban counties: San Francisco (4.3%; 95% CI =1.4, 7.1) and Alameda (3.8%; 95% CI = 1.7, 6.0). Hepatitis C Virus prevalenceincreased with decreasing income, reaching 5.1% (95% CI = 2.4,7.8) among women in the lowest income category (< $500 permonth). By race/ethnicity, Hepatitis C Virus prevalence was highest amongAfrican Americans (4.0%; 95% CI = 2.0, 5.9).
Women for whom sera were not available did not differ significantlyfrom women with sera with respect to age, education, income,or injection drug use history. However, women without sera
availablewere more likely to be single and to have 2 or more
male sexpartners and less likely to be Latina (2
test, P < .05).The latter finding resulted from sera
not being available fora disproportionate number of subjects
from San Joaquin County.
Prevalence of Anti-Hepatitis C Virus, by Sexually Transmitted Infections and Sexual
Behavior
Prevalence of Hepatitis C Virus was significantly higher among women with
serologic markers for infection with syphilis (18.3%; 95% CI
= 0, 41.7), HSV-2 (4.2%; 95% CI = 1.9, 6.4), HBV (8.3%; 95%
CI = 3.2, 13.5), and HIV (63.5%; 95% CI = 0.8, 119.5) (Table
2).
Prevalence of Hepatitis C Virus increased with increasing number of lifetimemale sexual partners, from 0.4% (95% CI = 0, 1.3) among womenwith 1 partner to 3.9% (95% CI = 2.2, 5.7) among women with5 or more partners. Only 2 women (0.1%) reported no male sexualpartners, and 1 of these women had Hepatitis C Virus infection. Other sexualrisk behaviors associated with increased Hepatitis C Virus prevalence weresex with an IDU (12.6%; 95% CI = 7.2, 18.0), exchange sex (tradingsex for money, drugs, or other needs) (13.6%; 95% CI = 5.6,18.6), and ever having anal sex (4.5%; 95% CI = 2.1, 7.0).
TABLE 2—Prevalence of Hepatitis C Virus (Hepatitis C Virus) Infection, by
Sexually Transmitted Infections and Reported Sexual Behavior, in Women
Aged 18 to 29 Years From Low-Income Neighborhoods of 4 Northern
California Counties, April 1996–January 1998
Population Prevalence of Variable, %a
Population Prevalence of Hepatitis C Virus Antibody, % (95% CI)a,b
Bivariate OR (95% CI)a
Chlamydia
3.2 2.1 (2.0, 6.2)
0.8 (0.1, 6.0)
Syphilis
0.8 18.3 (0, 41.7)
9.1 (1.7, 46.8)**
Gonorrhea
0.8 0 (NAc)
NAc
Herpes simplex virus type 2
34.2 4.2 (1.9, 6.4)
10.4 (3.2, 34.3)*
Hepatitis B (core antibody or surface antigen)
8.8 8.3 (3.2, 13.5)
4.1 (1.9, 8.8)*
HIV
0.2 63.5 (0.8, 119.5)
69.6 (6.1, 788.0)*
Lifetime male sex partners
1
19.8 0.4 (0, 1.3)
Referent
2–4
26.2 1.0 (0, 2.2)
1.4 (0.2, 8.5)
5
53.9 3.9 (2.2, 5.7)
5.6 (1.3, 23.8)**
Sex with injection drug user
10.3 12.6 (7.2, 18.0)
10.4 (5.8, 18.6)*
Traded sex for money or drugs
12.1 13.6 (5.6, 18.6)
14.5 (7.1, 29.7)*
Anal sex
22.9 4.5 (2.1, 7.0)
2.3 (1.3, 4.1)**
Note. CI = confidence interval; OR = odds ratio.
a
All prevalence estimates, 95% CIs, and ORs are adjusted
for the survey design.
b
Anti-Hepatitis C Virus confirmed in n = 40.
c
Not able to calculate survey-adjusted CIs or ORs when no
infections were detected.
*P.001;
**P.01.
Prevalence of Anti-Hepatitis C Virus, by Injection and Noninjection Drug Use
Table 3
shows the prevalence of Hepatitis C Virus among women by reportedalcohol,
noninjection drug, and injection drug use. Of note,the
estimate of lifetime injection drug use in the target populationwas 4.4% (95% CI = 2.9, 5.9). Hepatitis C Virus infection was strongly
associatedwith a history of injecting any drug (OR = 64.6;
95% CI = 33.0,126.2, P < .001). Hepatitis C Virus infection was
significantly more likelyamong women who reported sharing
needles in the past 6 monthscompared with those who did not
(66.7% vs 37.1%; OR = 3.3; 95%CI = 1.0, 11.0) but not among
women who reported having evershared a needle compared with
those who did not (OR = 2.7; 95%CI = 0.8, 10.1). Among women
with a history of injection druguse, the prevalence of Hepatitis C Virus
increased significantly with age:19.7% (95% CI = 5.5, 34.2)
among those younger than 24 yearsand 55% (95% CI = 38.0,
72.0) among those 24 years and older(data not shown).
TABLE 3—Prevalence of Hepatitis C Virus (Hepatitis C Virus) Infection, by
Alcohol, Noninjection Drug, and Injection Drug Use, in Women Aged 18 to
29 Years From Low-Income Neighborhoods of 4 Northern California
Counties, April 1996–January 1998
Population Prevalence of Variable, %a
Population Prevalence of Hepatitis C Virus Antibody, % (95% CI)a,b
OR (95% CI)a
History of injection drug use
4.4 37.5 (26.4, 48.6)
64.6 (33.0, 126.2)*
Shared needles (among those with a history of injection drug use)
Ever
56. 51.4 (38.8,63.9)
2.7 (0.8, 10.1)
Last 6 mo
38.5 66.7 (47.2, 86.1)
3.3 (1.0, 11.0)***
Alcohol
Ever
78.0 2.8 (1.6, 4.2)
2.2 (0.8, 6.3)
Last 6 mo
60.3 2.8 (1.5, 4.1)
1.3 (0.7, 2.7)
Sex while high on
30.7 4.3 (2.3, 6.4)
2.6 (1.3, 5.3)**
Amphetamine
Ever
12.3 9.3 (4.7, 13.8)
7.1 (4.0, 12.6)*
Last 6 mo
5.0 15.6 (8.3, 22.9)
9.8 (5.6, 17.4)*
Sex while high on
2. 19.7 (9.0, 30.3)
11.8 (6.0, 23.3)*
Injected amphetamine
Ever
2. 42.2 (26.5, 57.8)
43.8 (18.8, 102.0)*
Last 6 mo
1. 55.8 (34.8, 76.7)
61.9 (21.9, 174.7)*
Sex while high on
0.6 50.0 (28.1, 71.9)
44.0 (15.7, 123.5)*
Cocaine
Ever
17. 12.1 (7.8, 16.4)
27.6 (11.2, 67.9)*
Last 6 mo
8.5 21.1 (14.5, 27.7)
32.9 (15.8, 68.5)*
Sex while high on
5.2 24.5 (15.4, 33.7)
24.3 (11.2, 52.5)*
Injected cocaine
Ever
1.6 51.2 (33.2, 69.2)
59.9 (25.5, 140.8)*
Last 6 mo
0.9 72.6 (51.0, 94.2)
135.5 (40.7, 451.2)*
Sex while high on
0.2 100 (NAc)
NAc
Heroin
Ever
5.1 28.9 (18.8, 38.9)
36.2 (18.8, 68.9)*
Last 6 mo
2.2 50.4 (36.9, 64.1)
68.4 (32.7, 143.1)*
Sex while high on
1.5 51.7 (34.3, 69.1)
59.2 (25.9, 135.0)*
Injected heroin
Ever
2.8 44.5 (32.5, 56.5)
59.5 (28.5, 124.2)*
Last 6 mo
1.7 66.7 (56.1, 77.2)
140.6 (71.4, 276.6)*
Sex while high on
1.2 62.8 (51.2, 74.3)
93.4 (47.7, 182.8)*
Ever on methadone treatment
1.2 45.5 (21.3, 69.8)
41.0 (13.0, 129.8)*
Note. CI = confidence interval; OR = odds ratio.
a
All prevalence estimates, 95% CIs, and ORs are adjusted
for the survey design.
b
Anti-Hepatitis C Virus confirmed in n = 40.
c
Not able to calculate survey-adjusted CIs or ORs when the
point estimate is 0 or 100%.
*P.001;
**P.01;
***P.05.
Ever and recent use of alcohol was not associated with increasedHepatitis C Virus prevalence, but having had sex while high on alcohol was(OR = 2.6; 95% CI = 1.3, 5.3). Hepatitis C Virus prevalence was significantlyhigher among women reporting use of amphetamine, cocaine, orheroin compared with women not using these drugs. For each ofthese drugs, Hepatitis C Virus prevalence was higher among those reportingrecent use compared with ever use and among those reportinginjecting compared with those not injecting. Of any risk factormeasured, Hepatitis C Virus prevalence was highest among women reporting recentcocaine injection (72.6%; 95% CI = 51.0, 94.2), followed bythose reporting recent heroin injection (66.7%; 95% CI = 56.1,77.2).
Independent Risk Factors for Hepatitis C Virus Infection
In multivariate analyses (Table 4),
the strongest independentassociations with Hepatitis C Virus infection
were history of injection druguse (adjusted OR = 4.9; 95% CI
= 2.7, 9.2), serological evidenceof HSV-2 infection (OR =
3.7; 95% CI = 1.2, 11.5), any use ofheroin (OR = 5.6; 95% CI
= 3.1, 10.2), any use of cocaine (OR= 3.4; 95% CI = 1.2,
9.5), and very low income (adjusted ORfor income < $500 per
month = 4.2; 95% CI = 1.2, 14.4) afteradjustment for age.
Sexual risk behavior did not reach statisticalsignificance
in the model. The associations found between Hepatitis C Virusinfection
and race/ethnicity were confounded by income and reported
sexual risk behavior. African American women were most likely
to have HSV-2 infection, to have lower income, and to report
a history of trading sex for drugs or money and thus were at
highest risk for Hepatitis C Virus infection. No significant interactions
were found between age, racial/ethnic group, and sexual risk
behaviors. HIV infection was a significant risk factor for Hepatitis C Virusin this study but was excluded from the model because of smallnumbers and the observation that parameter estimates of theother variables were not significantly changed by its inclusion.The adjusted odds ratio for Hepatitis C Virus infection associated with HIVinfection was 7.5 (95% CI = 1.5, 37.0).
TABLE 4—Independent Associations With Hepatitis C Virus
Infection (Multivariate Analysis) and Associated Population Attributable
Fractions for Women Aged 18 to 29 Years From Low-Income Neighborhoods of
4 Northern California Counties, April 1996–January 1998
Adjusted OR (95% CI)
Adjusted Population Attributable Fraction (95% CI)
History of injection drug use
4.9 (2.7, 9.2)
0.332 (–0.9, 0.8)
Herpes simplex virus type 2
3.7 (1.2, 11.5)
0.506 (–13.8, 1.0)
Heroin use (ever)
5.6 (3.1, 10.2)
0.394 (–1.1, 0.8)
Cocaine use (ever)
3.4 (1.2, 9.5)
0.442 (–9.2, 1.0)
Age
(< 24 vs
24)
2.5 (0.9, 7.2)
Monthly income, $
< 500
4.2 (1.2, 14.4)
0.400 (–6.4, 1.0)
500–999
1.5 (0.3, 6.9)
0.695 (–2.3, 0.7)
1000
Referent
Note. CI = confidence interval; OR = odds ratio.
Analyses among women with no history of injection drug use wereconducted to evaluate risk factors associated with nonparenteralacquisition of Hepatitis C Virus infection. In this subset, 12 women (0.9%)were positive for anti-Hepatitis C Virus. Factors associated with Hepatitis C Virus amongwomen non-IDUs were African American race/ethnicity, noninjectioncocaine use, and lower income (Table 5).
Cocaine use and exchangesex (e.g., trading sex for money or
drugs) were highly collinear;however, cocaine use had a
stronger association. Among womennon-IDUs, African Americans
were significantly more likely (OR= 27.5; 95% CI = 3.4,
221.5) to be positive for Hepatitis C Virus than werenon–African American
women, an association confoundedby income level (the
unadjusted OR was 36.0).
TABLE 5—Independent Associations With Hepatitis C Virus
Infection Among Women Noninjection Drug Users (Multivariate Analysis)
Aged 18 to 29 Years From Low-Income Neighborhoods of 4 Northern
California Counties, April 1996–January 1998
Adjusted OR (95% CI)
Cocaine use (ever vs never)
6.6 (2.1, 20.9)
African American (vs other race/ethnicity)
27.5 (3.4, 221.5)
Monthly income, $
< 500
3.5 (0.4, 30.3)
500–999
3.1 (0.3, 27.5)
1000
Referent
Note. CI = confidence interval; OR = odds ratio.
Population Attributable Fraction Estimates
Adjusted population attributable fraction estimates and 95%
confidence intervals for risk factors for Hepatitis C Virus are shown in Table4.
History of injection drug use had an associated population
attributable fraction of 33.2%. The population attributable
fraction for HSV-2 infection was the highest (50.6%), reflectingthe high prevalence of the risk factor (34.2%). Both noninjectionheroin use and noninjection cocaine use had higher populationattributable fraction estimates than did injection history (39.4%and 44.2%, respectively), also because of their higher prevalence.In analyses excluding the effects of socioeconomic status andage, the summary population attributable fraction for these4 factors accounted for 91.0% of the Hepatitis C Virus cases. The summarypopulation attributable fraction for all of the risk factorsin the logistic model was 96.3%.
DISCUSSION
The 2.5% prevalence of Hepatitis C Virus infection in this population-based
survey of young, lowincome women was higher than that reportedin a national sample of women, in which prevalence was of 1.2%overall1
and 0.6% among women aged 20 to 29 years (M. Alter,PhD,
personal communication, 2000). Hepatitis C Virus infection was most highly
associated with a history of injection drug use, although noninjectionuse of heroin and cocaine persisted as independent risk factors.Hepatitis C Virus transmission has been hypothesized to occur through sharingof straws or other devices that deliver the virus to hyperemicand traumatized nasal mucosa.7
Very low income was the strongestsocioeconomic correlate of
Hepatitis C Virus infection. Of particular note,HSV-2 infection was
independently associated with Hepatitis C Virus infection.
The independent association of anti-Hepatitis C Virus with HSV-2 infectionsuggests a possible cofactor for sexual transmission or
acquisitionof Hepatitis C Virus. As has been hypothesized with HIV, HSV-2
infection mayserve to increase the efficiency of sexual
acquisition of Hepatitis C Virusinfection through enhanced viral
reproduction or by providinga portal of entry through
ulceration or inflammation. The cross-sectionaldesign of
this study, however, precludes confirmation of this
hypothesis and limits causal inference.
A similar association between Hepatitis C Virus and HSV-2 was shown in a studyof heterosexual couples who were Hepatitis C Virus serodiscordant.
Alteret al.
found that Hepatitis C Virus infection was associated with HSV-2 infection
in the National Health and Nutrition Examination Survey III
study in analyses controlling for age but not for drug use andhigh-risk sexual behaviors. Similarly, in a recent study amongdrug users in treatment, Hwang et al.
found no associationbetween Hepatitis C Virus and HSV-2 after controlling
for the confoundingeffects of injection history and sexual
risk.
We recognize that HSV-2 seropositivity may simply serve as abiological marker for underreported sexual risk in our study.However, understanding the role HSV-2 plays in Hepatitis C Virus infectioncould help reduce the potential sexual risk further and clarifyprevention messages regarding sexual transmission.
Furthermore,the high attributable risk suggests, first, that
if a causallink is established, HSV-2 infection may be an
important determinantof sexually acquired Hepatitis C Virus, and second,
that reducing exposurethrough condom use and treatment of
symptomatic genital herpesinfections could avert many
infections.
Attributable fraction estimates, which combine information onthe prevalence of the exposure with an associated measure ofexcess risk, provide an estimate of the potential effect ofpreventive interventions.
Our study suggested that althoughinjection drug use had a
significant excess risk associatedwith Hepatitis C Virus infection, the
higher prevalence of HSV-2 infectionand noninjection drug
use resulted in a larger population attributablefraction
estimate for these nonparenteral exposures. Resultsfurther
implied that prevention and control of Hepatitis C Virus infectionmust
focus not only on reducing injection drug use, which hasa
moderately low prevalence, but also on reducing sexually transmittedinfections and noninjection drug exposures. However, the etiologicinterpretation of population attributable fraction estimatesmust be approached with caution because of the wide confidenceintervals and potential noncausal associations. Given the modestsample size and the limited focus of the population under study(young women from low-income neighborhoods), the reader mustnot overinterpret the population attributable fraction estimates,which may be subject to both variability and the bias inherentin observational data. Measures of attributable risk providean important tool for public health planning and should notbe considered alternatives to measures of effect.
We recognize other possible limitations of the data. Only womenfor whom sera were available were included in the analyses,and although these women constituted 81.4% of the participatingsample, they represented only 60% of all the eligible womenidentified. No observations were made of nonparticipants; thus,nonresponse bias is possible. Comparisons of women with andwithout sera detected some differences; the most significantwas due to lack of sera from some women from San Joaquin County.Nonetheless, omitting San Joaquin from the analyses did notsubstantially change the principal findings of the study. Readersare also cautioned not to overinterpret results based on 40confirmed Hepatitis C Virus infections.
Despite these limitations, our data provide rare population-basedestimates of Hepatitis C Virus prevalence and related risk factors among young,low-income women. Understanding the epidemiology of Hepatitis C Virus infectionamong women in low-income neighborhoods is a critical firststep in designing primary and secondary interventions to mitigatethe morbidity and mortality of this emerging infection. Thegrowing evidence linking HSV-2 to HIV and HBVpoints to a potential role for HSV-2 as a cofactor in sexualtransmission of Hepatitis C Virus as well. Strong empirical evidence supportsthe efficacy of sexually transmitted infection control as ameans of reducing HIV risk through clinical and behavioral
intervention.Prevention of sexual transmission of Hepatitis C Virus should be consideredfrom a similar public health perspective. Although the per-contactlikelihood of Hepatitis C Virus transmission may be lower than through syringesharing, a large and growing pool of carriers may generate
significantnumbers of new infections through sexual
intercourse. Becausemany of the risk factors responsible for
Hepatitis C Virus infection are alsorelated to risk of other adverse
health outcomes, public healthefforts aimed at reducing drug
use and sexual risk vulnerabilityin very-low-income women
should have multiple positive results.
Acknowledgments
This work was supported in part by cooperative agreements U62/CCU0200,U62/CCU906250-06, U62/CU902019-12, and U61/CCU902019-13 fromthe Centers for Disease Control and Prevention. Additional fundingwas provided by the AIDS Office and the STD Prevention and ControlSection in the city and county of San Francisco.
We would like to acknowledge the following people for theirexpert assistance with this project: Dr Estie Hudes, for herexpert assistance with the population attributable fractionanalyses, and Drs Michael Busch and Andrew Moss, for reviewingthe manuscript and providing valuable comments. We thank thepublic health laboratory directors in the counties of Alameda,Contra Costa, San Francisco, San Mateo, and San Joaquin andthe staff of the Viral and Rickettsial Disease Laboratory forperforming specimen testing. We thank Dr Gail Bolan and Mr HaroldRasmussen for their support.
The Young Women's Survey Team also includes (in alphabeticalorder): Geneva Bell-Sanford, San Joaquin County Department ofPublic Health, Calif; Gail Bolan, San Francisco Department ofPublic Health, Calif; Cynthia Cossin, Viral and RickettsialDisease Laboratory, Berkeley, Calif; Viva Delgado, San FranciscoDepartment of Public Health, Calif; Carla Dillard Smith, CAL-PEP,Oakland, Calif; Maria Hernandez, San Francisco Department ofPublic Health, Calif; Tanya Holmes, Alameda County Departmentof Public Health, Calif; Martin Lynch, Contra Costa County
Departmentof Public Health, Calif; Juan Reardon, Contra
Costa County Departmentof Public Health, Calif; Charlotte
Smith, San Mateo County Departmentof Public Health, Calif;
Hypolitta Villa, San Joaquin CountyDepartment of Public
Health, Calif; and Francis Wiser, San MateoCounty Department
of Public Health, Calif.
The California Department of Health Services Institutional ReviewBoard and local institutional review boards, when available,approved all study protocols and materials.
Footnotes
K. A. Page-Shafer conceptualized and designed the study, analyzedand interpreted the data, and drafted the paper. B. Cahoon-Younghelped design the study and conducted laboratory analyses. J.D. Klausner contributed to interpretation of the data and tothe writing and critical revisions of the paper. S. Morrow andF. Molitor participated in acquisition of the data, administrativeand technical support, and revisions of the paper. J. Ruiz
contributedto obtaining funding, acquisition of the data,
administrativeand material support, and revisions of the
paper. W. McFarlandcontributed to conceptualizing the study,
designing the questionnaire,analyzing the data, and the
writing and revisions of the paper.The Young Women's Survey
Team contributed to the conceptionof the parent study,
acquisition of the data, and technicaland material support.
Peer Reviewed
Accepted for publication September 30, 2001.
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